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Failure prediction on railway turnouts using time delay neural networks

机译:基于时延神经网络的铁路道岔故障预测

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Turnout systems on railways play critical role on reliability of railway infrastructure. Identification and prediction of failures on mechanical systems have been attracting researchers and industry in recent years. Condition based maintenance focuses on failure identification and prediction using sensory information collected real-time from sensors embedded on electro-mechanical systems. This paper presents neural network based failure prediction algorithm on railway turnouts.
机译:铁路道岔系统对铁路基础设施的可靠性起着至关重要的作用。近年来,机械系统故障的识别和预测一直吸引着研究人员和工业界的注意。基于状况的维护着重于故障识别和预测,这些故障是使用从机电系统中嵌入的传感器实时收集的传感信息来进行的。本文提出了基于神经网络的铁路道岔故障预测算法。

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